Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning

@article{Skolik2022QuantumAI,
  title={Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning},
  author={Andrea Skolik and Sofi{\`e}ne Jerbi and Vedran Dunjko},
  journal={Quantum},
  year={2022},
  volume={6},
  pages={720}
}
Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. Research in this area has focused primarily on variational quantum algorithms (VQAs), and several proposals to enhance supervised, unsupervised and reinforcement learning (RL) algorithms with VQAs have been put forward. Out of the three, RL is the least studied and it is still an open question whether VQAs can be… 

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References

SHOWING 1-10 OF 64 REFERENCES

Variational Quantum Circuits for Deep Reinforcement Learning

This work reshapes classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits, and uses a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks.

Parametrized Quantum Policies for Reinforcement Learning

This work proposes a hybrid quantum-classical reinforcement learning model using very few qubits, which it is shown can be effectively trained to solve several standard benchmarking environments and formally proves the ability of parametrized quantum circuits to solve certain learning tasks that are intractable to classical models.

Quantum Enhancements for Deep Reinforcement Learning in Large Spaces

This work studies the state-of-the-art neural-network approaches for reinforcement learning with quantum enhancements in mind and demonstrates the substantial learning advantage that models with a sampling bottleneck can provide over conventional neural network architectures in complex learning environments.

Variational quantum policies for reinforcement learning

This work investigates how to construct and train reinforcement learning policies based on variational quantum circuits, and proposes and shows the existence of task environments with a provable separation in performance between quantum learning agents and any polynomial-time classical learner.

Exponential improvements for quantum-accessible reinforcement learning

The results suggest that quantum agents may perform well in certain game-playing scenarios, where the game has recursive structure, and the agent can learn by playing against itself.

TensorFlow Quantum: A Software Framework for Quantum Machine Learning

This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.

Quantum reinforcement learning in continuous action space

This work proposes an alternative quantum circuit design that can solve RL problems in continuous action space without the dimensionality problem and demonstrates that quantum control tasks, including the eigenvalue problem and quantum state transfer, can be formulated as sequential decision problems and solved by this method.

Quantum-accessible reinforcement learning beyond strictly epochal environments

This work considers one of the first generalizations of quantum-accessible reinforcement learning, where the environment is not strictly episodic, which is mapped to an oracle identification setting with a changing oracle.

Adaptive pruning-based optimization of parameterized quantum circuits

PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).

Classification with Quantum Neural Networks on Near Term Processors

This work introduces a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning, and shows through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets.
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